code: def test_execute_with_gradients(
      *,
      dtype_and_xs,
      retain_grads,
      test_flags,
      backend_fw,
      fn_name,
      on_device,
  ):
      if backend_fw == "numpy":
          return
  
      def func(xs):
          with BackendHandler.update_backend(
              ivy.current_backend(xs.to_native()).backend
          ) as ivy_backend:
              if isinstance(xs, ivy_backend.Container):
                  array_idxs = ivy_backend.nested_argwhere(
                      xs, ivy_backend.is_array
                  )
                  array_vals = ivy_backend.multi_index_nest(
                      xs, array_idxs
                  )
                  if len(array_vals) == 0:
                      final_array = None
                  else:
                      final_array = ivy_backend.stack(array_vals)
              else:
                  final_array = xs
              ret = ivy_backend.mean(final_array)
              return ret
  
      dtype, xs = dtype_and_xs
      helpers.test_function(
          input_dtypes=dtype,
          test_flags=test_flags,
          backend_to_test=backend_fw,
          fn_name=fn_name,
          func=func,
          rtol_=1e-1,
          atol_=1e-1,
          on_device=on_device,
          xs=xs[0],
          retain_grads=retain_grads,
      )
  
file_path: ivy/ivy_tests/test_ivy/test_functional/test_core/test_gradients.py
imports: from hypothesis import strategies
  import pytest
  import numpy
  import ivy
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import handle_test, BackendHandler
label_desc: Use image node_id 4 for calling a global function with example usage: test_execute_with_gradients() and returns: ret